Pretest–posttest–control group (PPC) effect sizes incorporate baseline information but require the pretest–posttest correlation for sampling-variance estimation, which is frequently unavailable in primary studies. This creates a practical trade-off: retaining baseline information requires an assumption about repeated-measures dependence, whereas using posttest-only effect sizes avoids this requirement but discards available baseline information. The present study used a simulation to evaluate whether missing correlations can be replaced by constant assumed values while maintaining acceptable variance calibration, and complemented the simulation with reanalyses of three published meta-analyses to illustrate the consequences for meta-analytic effects. Across 2,160 design conditions, we examined the sampling variances of two pretest-informed estimators (dppc1 and dppc2). Performance was evaluated in terms of coverage of nominal 95% confidence intervals and standard-error ratios. Across the fixed-correlation conditions, coverage declined from .97 to .83 for dppc1 and from .97 to .79 for dppc2 as the assumed correlation increased from .20 to .80, with corresponding standard-error ratios declining from 1.31 to 0.70 and from 1.32 to 0.68. Misspecification was asymmetric: overestimating the true correlation produced anti-conservative standard errors and undercoverage, whereas underestimating it generally produced conservative inference. Across the empirical examples, pooled PPC estimates were comparatively stable across correlation assumptions, whereas ignoring baseline information produced larger shifts in several pooled effects. Based on these findings, we provide practical guidelines for handling missing pretest–posttest correlations, emphasizing a hierarchical use of study-specific information, external evidence, and transparent sensitivity analyses when constant assumptions are required. An accompanying Shiny app supports these analyses: https://lliebenow.shinyapps.io/ppc-rho-sensitivity-explorer/.